探究Python多進程編程下線程之間變量的共享問題

更新時間：2015年05月05日 09:38:03 作者：xrzs

這篇文章主要介紹了探究Python多進程編程下線程之間變量的共享問題,多進程編程是Python學習進階中的重要知識,需要的朋友可以參考下

1、問題：

群中有同學貼了如下一段代碼，問為何 list 最后打印的是空值？

from multiprocessing import Process, Manager
import os
 
manager = Manager()
vip_list = []
#vip_list = manager.list()
 
def testFunc(cc):
  vip_list.append(cc)
  print 'process id:', os.getpid()
 
if __name__ == '__main__':
  threads = []
 
  for ll in range(10):
    t = Process(target=testFunc, args=(ll,))
    t.daemon = True
    threads.append(t)
 
  for i in range(len(threads)):
    threads[i].start()
 
  for j in range(len(threads)):
    threads[j].join()
 
  print "------------------------"
  print 'process id:', os.getpid()
  print vip_list

其實如果你了解 python 的多線程模型，GIL 問題，然后了解多線程、多進程原理，上述問題不難回答，不過如果你不知道也沒關系，跑一下上面的代碼你就知道是什么問題了。

python aa.py
process id: 632
process id: 635
process id: 637
process id: 633
process id: 636
process id: 634
process id: 639
process id: 638
process id: 641
process id: 640
------------------------
process id: 619
[]

將第 6 行注釋開啟，你會看到如下結果：

process id: 32074
process id: 32073
process id: 32072
process id: 32078
process id: 32076
process id: 32071
process id: 32077
process id: 32079
process id: 32075
process id: 32080
------------------------
process id: 32066
[3, 2, 1, 7, 5, 0, 6, 8, 4, 9]

2、python 多進程共享變量的幾種方式:
（1）Shared memory：
Data can be stored in a shared memory map using Value or Array. For example, the following code

http://docs.python.org/2/library/multiprocessing.html#sharing-state-between-processes

from multiprocessing import Process, Value, Array
 
def f(n, a):
  n.value = 3.1415927
  for i in range(len(a)):
    a[i] = -a[i]
 
if __name__ == '__main__':
  num = Value('d', 0.0)
  arr = Array('i', range(10))
 
  p = Process(target=f, args=(num, arr))
  p.start()
  p.join()
 
  print num.value
  print arr[:]

結果：

3.1415927
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

（2）Server process：

A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies.
A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Queue, Value and Array.
代碼見開頭的例子。

http://docs.python.org/2/library/multiprocessing.html#managers
3、多進程的問題遠不止這么多：數(shù)據的同步

看段簡單的代碼：一個簡單的計數(shù)器：

from multiprocessing import Process, Manager
import os
 
manager = Manager()
sum = manager.Value('tmp', 0)
 
def testFunc(cc):
  sum.value += cc
 
if __name__ == '__main__':
  threads = []
 
  for ll in range(100):
    t = Process(target=testFunc, args=(1,))
    t.daemon = True
    threads.append(t)
 
  for i in range(len(threads)):
    threads[i].start()
 
  for j in range(len(threads)):
    threads[j].join()
 
  print "------------------------"
  print 'process id:', os.getpid()
  print sum.value

結果：

------------------------
process id: 17378
97

也許你會問：WTF？其實這個問題在多線程時代就存在了，只是在多進程時代又杯具重演了而已：Lock！

from multiprocessing import Process, Manager, Lock
import os
 
lock = Lock()
manager = Manager()
sum = manager.Value('tmp', 0)
 
 
def testFunc(cc, lock):
  with lock:
    sum.value += cc
 
 
if __name__ == '__main__':
  threads = []
 
  for ll in range(100):
    t = Process(target=testFunc, args=(1, lock))
    t.daemon = True
    threads.append(t)
 
  for i in range(len(threads)):
    threads[i].start()
 
  for j in range(len(threads)):
    threads[j].join()
 
  print "------------------------"
  print 'process id:', os.getpid()
  print sum.value

這段代碼性能如何呢？跑跑看，或者加大循環(huán)次數(shù)試一下。。。
4、最后的建議：

Note that usually sharing data between processes may not be the best choice, because of all the synchronization issues; an approach involving actors exchanging messages is usually seen as a better choice. See also Python documentation: As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes. However, if you really do need to use some shared data then multiprocessing provides a couple of ways of doing so.

5、Refer：

http://stackoverflow.com/questions/14124588/python-multiprocessing-shared-memory

http://eli.thegreenplace.net/2012/01/04/shared-counter-with-pythons-multiprocessing/

http://docs.python.org/2/library/multiprocessing.html#multiprocessing.sharedctypes.synchronized

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